Advanced compiler optimizations for supercomputers
Communications of the ACM - Special issue on parallelism
Deadlock-Free Message Routing in Multiprocessor Interconnection Networks
IEEE Transactions on Computers
Data dependence and its application to parallel processing
International Journal of Parallel Programming
Synthesizing Linear Array Algorithms from Nested FOR Loop Algorithms
IEEE Transactions on Computers
Page table management in local/remote architectures
ICS '88 Proceedings of the 2nd international conference on Supercomputing
On the problem of optimizing data transfers for complex memory systems
ICS '88 Proceedings of the 2nd international conference on Supercomputing
Semi-automatic process partitioning for parallel computation
International Journal of Parallel Programming
Compiling C* programs for a hypercube multicomputer
PPEALS '88 Proceedings of the ACM/SIGPLAN conference on Parallel programming: experience with applications, languages and systems
Networks for parallel processors: measurements and prognostications
C3P Proceedings of the third conference on Hypercube concurrent computers and applications: Architecture, software, computer systems, and general issues - Volume 1
On partitioning and mapping for hypercube computing
International Journal of Parallel Programming
Dynamic Page Migration in Multiprocessors with Distributed Global Memory
IEEE Transactions on Computers
Minimum Distance: A Method for Partitioning Recurrences for Multiprocessors
IEEE Transactions on Computers
Utilizing Multidimensional Loop Parallelism on Large Scale Parallel Processor Systems
IEEE Transactions on Computers
Dependence graphs and compiler optimizations
POPL '81 Proceedings of the 8th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Pipelined Data Parallel Algorithms-I: Concept and Modeling
IEEE Transactions on Parallel and Distributed Systems
Tiling multidimensional iteration spaces for nonshared memory machines
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
Executing DSP Applications in a Fine-Grained Dataflow Environment
IEEE Transactions on Software Engineering
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
Chain Grouping: A Method for Partitioning Loops onto Mesh-Connected Processor Arrays
IEEE Transactions on Parallel and Distributed Systems
Proceedings of the thirteenth annual ACM symposium on Parallel algorithms and architectures
Optimal tiling for the RNA base pairing problem
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
Pipelined Data Parallel Algorithms-I: Concept and Modeling
IEEE Transactions on Parallel and Distributed Systems
Partitioning and Mapping Nested Loops on Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
Optimal Processor Assignment for a Class of Pipelined Computations
IEEE Transactions on Parallel and Distributed Systems
Communication-Free Data Allocation Techniques for Parallelizing Compilers on Multicomputers
IEEE Transactions on Parallel and Distributed Systems
A General Methodology of Partitioning and Mapping for Given Regular Arrays
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Hyperplane Grouping and Pipelined Schedules: How to Execute Tiled Loops Fast on Clusters of SMPs
The Journal of Supercomputing
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
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A methodology for designing pipelined data-parallel algorithms on multicomputers is studied. The design procedure starts with a sequential algorithm which can be expressed as a nested loop with constant loop-carried dependencies. The procedure's main focus is on partitioning the loop by grouping related iterations together. Grouping is necessary to balance the communication overhead with the available parallelism and to produce pipelined execution patterns, which result in pipelined data-parallel computations. The grouping should satisfy dependence relationships among the iterations and also allow the granularity to be controlled. Various properties of grouping are studied, and methods for generating communication-efficient grouping are given. Given a grouping and an assignment of the groups to the processors, an analytic model is combined with the grouping results to describe the behavior and to estimate the performance of the resultant parallel program. Expressions characterizing the performance are derived.